Since LF image SR methods can super-resolve all perspectives of an LF, we further analyze the performance of these methods of each SAI on scene "Rusty Fency" from the EPFL [1]} dataset. Due to the occlusions and specularities, the content of different SAIs is not identical, resulting in varying PSNRs among different SAIs. Since SISR methods super-resolve each SAI independently without using the complementary information, we only compare the PSNR of each SAI of learning-based LFSR methods. As shown in Fig. REF , we visually compare \(9\times 9\)  SAIs on scene "Rusty Fency" for \(2\times \)  SR. It can be observed that our method achieves better performance with a relatively balanced distribution. That is because, our method can fully exploit the complementary information among different views and channels by using our dense dual-attention module.
